
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

# Gráficos
# ==============================================================================
import matplotlib.pyplot as plt

from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
plt.style.use('fivethirtyeight')

%matplotlib inline


netflix = pd.read_csv("netflix_titles.csv")


netflix.head()


fig = px.histogram(netflix, x="release_year",title="Cantidad de contenido publicado por año")
fig.show()


movies = netflix[netflix["type"] == "Movie"].copy()
movies["duration"] = movies["duration"].astype(str)
movies["duration"] = movies["duration"].map(lambda x: x.replace("min", "")).str.strip()
movies= movies[movies.duration.apply(lambda x: x.isnumeric())]
movies["duration"] = movies["duration"].astype(int)


fig = px.histogram(movies,x = "duration", nbins = 90, title="Cantidad de películas según su duración")
fig.show()


fig = px.histogram(netflix,x="rating", color = "type", title= "Cantidad de series y películas según su clasificación")
fig.show()


most_common_countries = netflix.country.value_counts()[:10]
fig = px.bar(most_common_countries,x="country", color= "country", title= "Cantidad de películas por pais")
fig.update_yaxes(title_text='Country')
fig.show()


datadict = {}
for year in movies.release_year.unique() :
    moviesthisyear = movies.duration[movies["release_year"] == year]
    meanduration = ((moviesthisyear.sum())/moviesthisyear.count())
    datadict[year] = meanduration


durbyagedf = pd.DataFrame(datadict.items(), columns=['Year', 'Duration'])


plt.figure(figsize = (10,5))
fig = sns.lineplot(data = durbyagedf, x = "Year", y = "Duration")
plt.title("Duración media de las películas según su año")

Text(0.5, 1.0, 'Duración media de las películas según su año')


most_listed_genres = netflix.listed_in.value_counts()[:10]


fig = px.funnel(most_listed_genres,x="listed_in", color= "listed_in", title= "Cantidad de películas y series por género")
fig.update_yaxes(title_text='Genre')
fig.show()


post2000netflix = netflix[netflix.release_year >2000]


fig = px.violin(post2000netflix, y="release_year", x="type", color = "type", box=True, points="all", hover_data=netflix.columns, title = "Cantidad de películas y series publicadas por año")
fig.show()


datadict = {}
movies =  movies[movies.rating.isna()==False]
for rating in movies.rating.unique() :
    moviesrating = movies.duration[movies["rating"] == rating]
    meanduration = ((moviesrating.sum())/moviesrating.count())
    datadict[rating] = meanduration
durbyrating = pd.DataFrame(datadict.items(), columns=['Rating', 'Duration'])


plt.figure(figsize = (10,5))
fig = sns.barplot(data = durbyrating, x = "Rating", y = "Duration")
plt.title("Duración media de cada clasificación")

Text(0.5, 1.0, 'Duración media de cada clasificación')

	show_id	type	title	director	cast	country	date_added	release_year	rating	duration	listed_in	description
0	s1	Movie	Dick Johnson Is Dead	Kirsten Johnson	NaN	United States	September 25, 2021	2020	PG-13	90 min	Documentaries	As her father nears the end of his life, filmm...
1	s2	TV Show	Blood & Water	NaN	Ama Qamata, Khosi Ngema, Gail Mabalane, Thaban...	South Africa	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, TV Dramas, TV Mysteries	After crossing paths at a party, a Cape Town t...
2	s3	TV Show	Ganglands	Julien Leclercq	Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabi...	NaN	September 24, 2021	2021	TV-MA	1 Season	Crime TV Shows, International TV Shows, TV Act...	To protect his family from a powerful drug lor...
3	s4	TV Show	Jailbirds New Orleans	NaN	NaN	NaN	September 24, 2021	2021	TV-MA	1 Season	Docuseries, Reality TV	Feuds, flirtations and toilet talk go down amo...
4	s5	TV Show	Kota Factory	NaN	Mayur More, Jitendra Kumar, Ranjan Raj, Alam K...	India	September 24, 2021	2021	TV-MA	2 Seasons	International TV Shows, Romantic TV Shows, TV ...	In a city of coaching centers known to train I...

PEC 3 - Visualización de datos